{"id":"https://openalex.org/W4387969623","doi":"https://doi.org/10.1145/3581783.3611935","title":"Follow-me: Deceiving Trackers with Fabricated Paths","display_name":"Follow-me: Deceiving Trackers with Fabricated Paths","publication_year":2023,"publication_date":"2023-10-26","ids":{"openalex":"https://openalex.org/W4387969623","doi":"https://doi.org/10.1145/3581783.3611935"},"language":"en","primary_location":{"id":"doi:10.1145/3581783.3611935","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3581783.3611935","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3581783.3611935","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 31st ACM International Conference on Multimedia","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://dl.acm.org/doi/pdf/10.1145/3581783.3611935","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5010090922","display_name":"Shengtao Lou","orcid":"https://orcid.org/0009-0008-2043-5526"},"institutions":[{"id":"https://openalex.org/I50760025","display_name":"Hangzhou Dianzi University","ror":"https://ror.org/0576gt767","country_code":"CN","type":"education","lineage":["https://openalex.org/I50760025"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Shengtao Lou","raw_affiliation_strings":["Hangzhou Dianzi University, Hangzhou, China"],"raw_orcid":"https://orcid.org/0009-0008-2043-5526","affiliations":[{"raw_affiliation_string":"Hangzhou Dianzi University, Hangzhou, China","institution_ids":["https://openalex.org/I50760025"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5089927825","display_name":"Buyu Liu","orcid":"https://orcid.org/0009-0004-5534-7463"},"institutions":[{"id":"https://openalex.org/I4210107353","display_name":"NEC (United States)","ror":"https://ror.org/01v791m31","country_code":"US","type":"company","lineage":["https://openalex.org/I118347220","https://openalex.org/I4210107353"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Buyu Liu","raw_affiliation_strings":["NEC Laboratories America, San Jose, CA, USA"],"raw_orcid":"https://orcid.org/0009-0004-5534-7463","affiliations":[{"raw_affiliation_string":"NEC Laboratories America, San Jose, CA, USA","institution_ids":["https://openalex.org/I4210107353"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101680655","display_name":"Jun Bao","orcid":"https://orcid.org/0009-0005-3766-3953"},"institutions":[{"id":"https://openalex.org/I50760025","display_name":"Hangzhou Dianzi University","ror":"https://ror.org/0576gt767","country_code":"CN","type":"education","lineage":["https://openalex.org/I50760025"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jun Bao","raw_affiliation_strings":["Hangzhou Dianzi University, Hangzhou, China"],"raw_orcid":"https://orcid.org/0009-0005-3766-3953","affiliations":[{"raw_affiliation_string":"Hangzhou Dianzi University, Hangzhou, China","institution_ids":["https://openalex.org/I50760025"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5081149043","display_name":"Jiajun Ding","orcid":"https://orcid.org/0000-0002-7497-7485"},"institutions":[{"id":"https://openalex.org/I50760025","display_name":"Hangzhou Dianzi University","ror":"https://ror.org/0576gt767","country_code":"CN","type":"education","lineage":["https://openalex.org/I50760025"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jiajun Ding","raw_affiliation_strings":["Hangzhou Dianzi University, Hangzhou, China"],"raw_orcid":"https://orcid.org/0000-0002-7497-7485","affiliations":[{"raw_affiliation_string":"Hangzhou Dianzi University, Hangzhou, China","institution_ids":["https://openalex.org/I50760025"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5050817770","display_name":"Jun Yu","orcid":"https://orcid.org/0000-0003-1922-7283"},"institutions":[{"id":"https://openalex.org/I50760025","display_name":"Hangzhou Dianzi University","ror":"https://ror.org/0576gt767","country_code":"CN","type":"education","lineage":["https://openalex.org/I50760025"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jun Yu","raw_affiliation_strings":["Hangzhou Dianzi University, Hanzhou, China"],"raw_orcid":"https://orcid.org/0000-0003-1922-7283","affiliations":[{"raw_affiliation_string":"Hangzhou Dianzi University, Hanzhou, China","institution_ids":["https://openalex.org/I50760025"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":{"value":0.15399446,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"8808","last_page":"8818"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.9987999796867371,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.9987999796867371,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10331","display_name":"Video Surveillance and Tracking Methods","score":0.9987000226974487,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9969000220298767,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7888767123222351},{"id":"https://openalex.org/keywords/bittorrent-tracker","display_name":"BitTorrent tracker","score":0.7473390698432922},{"id":"https://openalex.org/keywords/counterfeit","display_name":"Counterfeit","score":0.7186042070388794},{"id":"https://openalex.org/keywords/focus","display_name":"Focus (optics)","score":0.6228258609771729},{"id":"https://openalex.org/keywords/offset","display_name":"Offset (computer science)","score":0.6169869899749756},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5501769185066223},{"id":"https://openalex.org/keywords/frame","display_name":"Frame (networking)","score":0.5306101441383362},{"id":"https://openalex.org/keywords/adversarial-system","display_name":"Adversarial system","score":0.5256372690200806},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.5084182620048523},{"id":"https://openalex.org/keywords/path","display_name":"Path (computing)","score":0.48385894298553467},{"id":"https://openalex.org/keywords/object","display_name":"Object (grammar)","score":0.44393399357795715},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.43874549865722656},{"id":"https://openalex.org/keywords/object-detection","display_name":"Object detection","score":0.41624248027801514},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.3381255865097046},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.29877740144729614},{"id":"https://openalex.org/keywords/eye-tracking","display_name":"Eye tracking","score":0.11076653003692627}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7888767123222351},{"id":"https://openalex.org/C57501372","wikidata":"https://www.wikidata.org/wiki/Q2021268","display_name":"BitTorrent tracker","level":3,"score":0.7473390698432922},{"id":"https://openalex.org/C2779356469","wikidata":"https://www.wikidata.org/wiki/Q502918","display_name":"Counterfeit","level":2,"score":0.7186042070388794},{"id":"https://openalex.org/C192209626","wikidata":"https://www.wikidata.org/wiki/Q190909","display_name":"Focus (optics)","level":2,"score":0.6228258609771729},{"id":"https://openalex.org/C175291020","wikidata":"https://www.wikidata.org/wiki/Q1156822","display_name":"Offset (computer science)","level":2,"score":0.6169869899749756},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5501769185066223},{"id":"https://openalex.org/C126042441","wikidata":"https://www.wikidata.org/wiki/Q1324888","display_name":"Frame (networking)","level":2,"score":0.5306101441383362},{"id":"https://openalex.org/C37736160","wikidata":"https://www.wikidata.org/wiki/Q1801315","display_name":"Adversarial system","level":2,"score":0.5256372690200806},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.5084182620048523},{"id":"https://openalex.org/C2777735758","wikidata":"https://www.wikidata.org/wiki/Q817765","display_name":"Path (computing)","level":2,"score":0.48385894298553467},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.44393399357795715},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.43874549865722656},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.41624248027801514},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3381255865097046},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.29877740144729614},{"id":"https://openalex.org/C56461940","wikidata":"https://www.wikidata.org/wiki/Q970687","display_name":"Eye tracking","level":2,"score":0.11076653003692627},{"id":"https://openalex.org/C17744445","wikidata":"https://www.wikidata.org/wiki/Q36442","display_name":"Political science","level":0,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.0},{"id":"https://openalex.org/C120665830","wikidata":"https://www.wikidata.org/wiki/Q14620","display_name":"Optics","level":1,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C199539241","wikidata":"https://www.wikidata.org/wiki/Q7748","display_name":"Law","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3581783.3611935","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3581783.3611935","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3581783.3611935","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 31st ACM International Conference on Multimedia","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.1145/3581783.3611935","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3581783.3611935","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3581783.3611935","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 31st ACM International Conference on Multimedia","raw_type":"proceedings-article"},"sustainable_development_goals":[{"score":0.8299999833106995,"display_name":"Peace, Justice and strong institutions","id":"https://metadata.un.org/sdg/16"}],"awards":[{"id":"https://openalex.org/G3236026856","display_name":null,"funder_award_id":"62206082","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G6151690139","display_name":null,"funder_award_id":"62020106007","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G6570941292","display_name":null,"funder_award_id":"62125201","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4387969623.pdf","grobid_xml":"https://content.openalex.org/works/W4387969623.grobid-xml"},"referenced_works_count":33,"referenced_works":["https://openalex.org/W639708223","https://openalex.org/W2062555005","https://openalex.org/W2158592639","https://openalex.org/W2159128898","https://openalex.org/W2194775991","https://openalex.org/W2424629859","https://openalex.org/W2543927648","https://openalex.org/W2604505099","https://openalex.org/W2618530766","https://openalex.org/W2799058067","https://openalex.org/W2886910176","https://openalex.org/W2916780012","https://openalex.org/W2943047381","https://openalex.org/W2963534981","https://openalex.org/W3034418285","https://openalex.org/W3035511673","https://openalex.org/W3035571898","https://openalex.org/W3035672751","https://openalex.org/W3035677743","https://openalex.org/W3044951323","https://openalex.org/W3089134182","https://openalex.org/W3127796792","https://openalex.org/W3164347164","https://openalex.org/W3167536469","https://openalex.org/W3168663926","https://openalex.org/W3170378827","https://openalex.org/W3172670627","https://openalex.org/W4220857797","https://openalex.org/W4224918372","https://openalex.org/W4232801258","https://openalex.org/W4312651496","https://openalex.org/W6603944243","https://openalex.org/W6605844419"],"related_works":["https://openalex.org/W3191226418","https://openalex.org/W2357749344","https://openalex.org/W2502115930","https://openalex.org/W4323356230","https://openalex.org/W2362200800","https://openalex.org/W1539823648","https://openalex.org/W4290078996","https://openalex.org/W3113783116","https://openalex.org/W4372352523","https://openalex.org/W1579156572"],"abstract_inverted_index":{"Convolutional":[0],"Neural":[1],"Networks":[2],"(CNNs)":[3],"are":[4,115,162],"vulnerable":[5],"to":[6,38,72,77,117,144,164,191],"adversarial":[7,22,172],"attacks":[8,23],"in":[9,24,104,132,170],"which":[10,120,242],"visually":[11],"imperceptible":[12],"perturbations":[13],"can":[14],"deceive":[15],"CNN-based":[16],"models.":[17,90],"While":[18],"current":[19],"research":[20],"on":[21,84,201,233],"single":[25],"object":[26],"tracking":[27],"exists,":[28],"it":[29,99],"overlooks":[30],"a":[31,140],"critical":[32],"aspect":[33],"of":[34,43,47,65,110,124,182],"manipulating":[35],"predicted":[36,153],"trajectories":[37,74],"follow":[39],"user-defined":[40],"paths":[41,184,240],"regardless":[42],"the":[44,48,56,108,122,128,133,146,156,166,193,238],"actual":[45],"location":[46,126,131],"targeted":[49],"object.":[50],"To":[51,174],"address":[52],"this,":[53],"we":[54,82,96,138,178],"propose":[55],"very":[57],"first":[58,97],"white-box":[59],"attack":[60,247],"algorithm":[61,213],"that":[62,75,211],"is":[63,243,252],"capable":[64],"deceiving":[66],"victim":[67,89,199],"trackers":[68,86],"by":[69,159],"compelling":[70],"them":[71],"generate":[73],"adhere":[76],"predetermined":[78],"counterfeit":[79,94,183,239],"paths.":[80],"Specifically,":[81],"focus":[83],"Siamese-based":[85],"as":[87,185,187],"our":[88,152,171,176,212],"Given":[91],"an":[92],"arbitrary":[93],"path,":[95],"decompose":[98],"into":[100],"discrete":[101],"target":[102,129],"locations":[103,114],"each":[105],"frame,":[106],"with":[107,197],"assumption":[109],"constant":[111],"velocity.":[112],"These":[113],"converted":[116],"heatmap":[118],"anchors,":[119],"represent":[121],"offset":[123],"their":[125],"from":[127],"object's":[130],"previous":[134],"frame.":[135],"Later":[136],"on,":[137],"design":[139,179],"novel":[141,188],"loss":[142,161],"function":[143],"minimize":[145],"gap":[147],"between":[148],"above-mentioned":[149],"anchors":[150],"and":[151,208,226,229],"ones.":[154],"Finally,":[155],"gradients":[157],"computed":[158],"such":[160],"used":[163],"update":[165],"original":[167],"video,":[168],"resulting":[169],"video.":[173],"validate":[175],"ideas,":[177],"three":[180,202],"sets":[181],"well":[186],"evaluation":[189,222],"metrics":[190],"measure":[192],"path-following":[194],"properties.":[195],"Experiments":[196],"two":[198],"models":[200],"publicly":[203],"available":[204,253],"datasets,":[205],"OTB100,":[206,234],"VOT2018,":[207],"VOT2016,":[209],"demonstrate":[210],"not":[214],"only":[215],"outperforms":[216],"SOTA":[217],"methods":[218],"significantly":[219],"under":[220],"conventional":[221],"metrics,":[223],"e.g.":[224],"90%":[225],"68.4%":[227],"precision":[228],"successful":[230],"rate":[231],"drop":[232],"but":[235],"also":[236],"follows":[237],"well,":[241],"beyond":[244],"any":[245],"existing":[246],"methods.":[248],"The":[249],"source":[250],"code":[251],"at":[254],"https://github.com/loushengtao/Follow-me.":[255]},"counts_by_year":[],"updated_date":"2026-08-22T07:34:49.880490","created_date":"2025-10-10T00:00:00"}
